Large Language Models aren't just transforming Silicon Valley—they're revolutionizing industries that have operated the same way for decades. From farms to factories, hospitals to energy plants, LLMs are delivering measurable results that go straight to the bottom line. Let's explore how six traditional industries are using AI to solve real problems and create competitive advantages.
Agriculture: Making Expert Knowledge Accessible
Farmers worldwide are gaining access to expert agronomic advice that was previously available only to large operations. Bayer developed an LLM-based agronomy advisor trained on decades of crop data that can answer complex farm management questions in seconds—a process that normally took hours or days.
The impact on smallholder farmers is even more dramatic. Digital Green's Farmer.Chat, powered by GPT-4, has reduced the cost of serving farmers from $35 to just $0.35—a 100× improvement. This democratization of expert farming knowledge is already translating to 24% higher farmer incomes on average.
Healthcare: Giving Doctors Their Time Back
Healthcare professionals are reclaiming time to focus on what matters most: patient care. The transformation is dramatic: Kaiser Permanente's ambient AI scribes transcribed and summarized conversations for over 2.5 million patient visits in one year, saving physicians 15,791 hours of documentation time—equivalent to nearly 1,800 workdays.
The impact goes beyond time savings. Doctors report the AI freed them from "pajama time" spent on charts at home, allowing them to focus on patients during visits. The results speak volumes: 84% of physicians said it improved doctor-patient communication, 82% felt more satisfied with their work, and nearly half of patients noticed their doctor spent less time on the computer during visits. By reducing burnout and giving doctors back time, these AI scribes helped "restore the human side of medicine."
At Mayo Clinic, AI-assisted patient message responses are saving nurses 30 seconds per message while making replies more empathetic. When you handle thousands of portal messages, those time savings add up significantly. Healthcare providers across the US and Europe are rapidly expanding these use cases, from AI-assisted medical coding to LLM-powered literature search assistants, all aimed at boosting productivity and patient outcomes.
Energy: Unlocking Decades of Technical Knowledge
Energy companies sit on vast repositories of technical data that have been difficult to access—until now. Shell built a custom LLM chatbot with NVIDIA's NeMo framework to help engineers quickly query internal research documents. By fine-tuning the model on decades of Shell's chemistry and engineering data, they achieved a 30% increase in answer accuracy versus the base model. This domain-trained LLM can retrieve precise technical answers that generic models would miss, reducing time spent searching manuals and reports while reducing errors in decision-making.
Shell is now exploring multimodal features to analyze images and charts, further aiding engineers and geoscientists. The AI assistant is speeding up access to specialized knowledge that was previously locked away in documents.
In upstream operations, LLMs are turning historical drilling reports into actionable insights. One oil producer integrated an LLM-based chatbot with thousands of historical drilling reports to assist in well diagnostics. The chatbot could answer complex queries by parsing these reports and even generate SQL queries to pull specific data. During testing, this AI assistant identified operational improvement opportunities—for instance, it found certain wells using plunger lifts had about 20% higher production than others, highlighting best practices to apply more broadly.
Energy firms across the U.S. and EU are using generative AI for drilling optimization (LLMs synthesize sensor data and recommend drilling parameters) and maintenance planning (LLM assistants parse maintenance logs and predict equipment failures). These early wins show that even in heavy industries, LLMs can turn data into actionable knowledge, improving efficiency and safety.
Manufacturing: From Paperwork to Productivity
Manufacturing companies are automating their most tedious processes with impressive results.Siemens faced a tedious process handling thousands of supplier delivery notes in its factories. By deploying a generative AI solution with LLM-powered document understanding, Siemens achieved over 90% "touchless" processing of delivery notes within just two weeks. The AI reads and extracts data from over 35,000 different delivery note layouts with 98% accuracy, auto-populating their ERP system.
This virtually eliminated manual data entry—a Siemens manager said they were "shocked by the leap in performance" as the AI reached next-level straight-through processing. The impact was faster throughput, fewer errors, and higher employee satisfaction as staff could focus on more strategic work instead of typing up forms. Automating this formerly labor-intensive task improved Siemens' operational efficiency and data quality in the supply chain.
BMW uses generative AI to create synthetic training images of parts and assemblies, which, coupled with no-code AI tools, cut the time for employees to build computer vision models by two-thirds. This means faster deployment of visual QA checks (like AI that inspects if the correct door sill or stitch color is used) and fewer defects escaping into finished products.
Additionally, AI copilots help engineers and technicians by answering questions about machine manuals or suggesting solutions to production problems using company knowledge. NVIDIA noted an 8× boost in BMW's data scientists' productivity after scaling their AI infrastructure, enabling rapid iteration of models. From back-office paperwork to the assembly line, generative AI is driving faster processes, lower error rates, and upskilled workers in manufacturing.
Real Estate: Making Data Mountains Manageable
Real estate, traditionally a tech-laggard, is seeing newfound efficiency through LLMs. Firms are sitting on mountains of documents—leases, property records, market reports—and generative AI is helping unlock this data. Lease analysis is a prime example: Real estate asset managers can use LLM-powered tools to instantly summarize key terms across hundreds of lease contracts. Instead of manually sifting through clauses, an LLM can pull answers to questions like "Which leases have rent escalation below 2%?" or "What's the total monthly rent across all retail tenants?"
According to McKinsey, applying genAI in this way has enabled real estate companies to improve decision-making and even boost net operating income by over 10% through more efficient operations and smarter asset management. The ability to quickly analyze portfolios and identify opportunities (or risks) at "lightning speed" is transforming how investors and property managers operate.
Customer-facing applications are emerging too. Conversational AI chatbots are being used by property management companies to handle tenant inquiries and maintenance requests. These AI assistants can answer common questions ("When is rent due?") or even help schedule repairs, reserving human staff for complex issues.
A Stanford study found that a GPT-based support tool led to a 14% increase in productivity for customer service agents, essentially getting new agents up to speed 2× faster (novices performed at the level of reps with several more months of experience). In real estate, this means leasing agents or support reps augmented by AI can respond faster and more accurately to prospective renters or owners. Listing platforms are using genAI to improve the customer journey—for instance, Zillow's ChatGPT plugin allows homebuyers to describe their ideal home in plain language and get tailored listing recommendations.
Transportation & Logistics: Optimizing Complex Networks
Transportation and logistics companies are turning to AI/LLMs to optimize routes, enhance customer service, and cut through paperwork—all driving better efficiency in a historically low-margin sector. Generative AI assistants can help manage the complexity of global supply chains by analyzing real-time data and answering planning questions in natural language.Microsoft reports that AI-powered innovations could reduce overall logistics costs by ~15%, optimize inventory levels by ~35%, and boost service levels by ~65% for logistics providers.
These gains come from better demand forecasting, dynamic route optimization, and automating routine decisions—essentially running a leaner, more responsive supply chain. For example, the European retailer SPAR implemented an AI-driven forecasting solution that achieved over 90% forecast accuracy for store sales, reducing food waste and cutting costs by about 15% by avoiding overstocking. Transportation firms are similarly using AI to adjust delivery routes on the fly (saving fuel and time) and to maximize loads.
On the customer service side, LLMs are improving the experience for shippers and travelers. Virtual agents can handle shipment tracking queries or travel reservations conversationally. The global retailer Decathlon deployed an AI voicebot for its e-commerce logistics that triages customer calls about orders and deliveries. The result was a 20% reduction in calls that had to be passed to human agents, thanks to the AI resolving common inquiries automatically. This not only lowers support costs but also speeds up response times for customers.
In air travel, airlines are piloting GPT-based chatbots to answer passenger questions and even assist agents with rebooking during disruptions. In ground transport, trucking companies are using LLM-driven document processing to handle bills of lading and customs forms faster. One freight carrier reported that automating bill-of-lading processing with AI cut manual effort by over 80%, accelerating shipments and reducing errors.
Crucially, LLMs can bridge data silos in logistics. Platforms like project44's Movement GPT allow supply chain managers to ask questions like "Show all shipments in northern Europe delayed by weather" and get instant answers by scanning multiple systems. This kind of conversational visibility was unheard of before. As these technologies mature, we're approaching the vision of an "autonomous, self-healing" supply chain where AI anticipates disruptions and prescribes solutions in real time.
The Results Speak for Themselves
These aren't theoretical benefits—they're measurable outcomes happening right now. We're seeing double-digit efficiency gains, significant cost savings, faster cycle times, and improved experiences for both employees and customers. What's remarkable is that most of these wins have emerged in just the past 1-2 years.
Many traditional businesses are still in the pilot phase, which means even greater productivity gains lie ahead as deployments scale. The companies that thoughtfully integrate LLMs into their operations today will have a significant competitive advantage tomorrow.
Ready to Transform Your Industry?
The pattern is clear: LLMs work best when they're tailored to your specific domain, data, and challenges. At Rocket Labs, we help traditional industry leaders navigate this transformation thoughtfully—from evaluation and proof-of-concepts to production integration and ongoing monitoring.
Learn more about our services and how we can help you turn your industry expertise into your competitive advantage.